Tidyverse y Ggplot

library(palmerpenguins) #Set de datos para ejemplos
head(penguins)

## # A tibble: 6 x 8
##   species island bill_length_mm bill_depth_mm flipper_length_~ body_mass_g sex  
##   <fct>   <fct>           <dbl>         <dbl>            <int>       <int> <fct>
## 1 Adelie  Torge~           39.1          18.7              181        3750 male 
## 2 Adelie  Torge~           39.5          17.4              186        3800 fema~
## 3 Adelie  Torge~           40.3          18                195        3250 fema~
## 4 Adelie  Torge~           NA            NA                 NA          NA <NA> 
## 5 Adelie  Torge~           36.7          19.3              193        3450 fema~
## 6 Adelie  Torge~           39.3          20.6              190        3650 male 
## # ... with 1 more variable: year <int>

Básicamente un data frame con mas información.

library(tidyverse)

## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --

## v ggplot2 3.3.6     v purrr   0.3.4
## v tibble  3.1.6     v dplyr   1.0.8
## v tidyr   1.2.0     v stringr 1.4.0
## v readr   2.1.2     v forcats 0.5.1

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

palmerpenguins:: penguins %>%
  group_by(species) %>% #Eligiendo la agrupación que nos interesa
  summarize(across(where(is.numeric), mean, na.rm = TRUE))

## # A tibble: 3 x 6
##   species   bill_length_mm bill_depth_mm flipper_length_mm body_mass_g  year
##   <fct>              <dbl>         <dbl>             <dbl>       <dbl> <dbl>
## 1 Adelie              38.8          18.3              190.       3701. 2008.
## 2 Chinstrap           48.8          18.4              196.       3733. 2008.
## 3 Gentoo              47.5          15.0              217.       5076. 2008.

# Calculando la media por grupo para cada categoría

mass_flipper <- ggplot(data = penguins,
                       aes(x = flipper_length_mm,
                           y = body_mass_g)) +
  geom_point(aes(color = species,
                 shape = species),
             size = 3,
             alpha = 0.8) +
  theme_minimal() +
  scale_color_manual(values = c("darkorange","purple","cyan4")) +
  labs(title = "Penguin size, Palmer Station LTER",
       subtitle = "Flipper length and body mass for Adelie, Chinstrap and Gentoo Penguins",
       x = "Flipper length (mm)",
       y = "Body mass (g)",
       color = "Penguin species",
       shape = "Penguin species") +
  theme(legend.position = c(0.2, 0.7),
        legend.background = element_rect(fill = "white", color = NA),
        plot.title.position = "plot",
        plot.caption = element_text(hjust = 0, face= "italic"),
        plot.caption.position = "plot")

mass_flipper

bill_len_dep <- ggplot(data = penguins,
                         aes(x = bill_length_mm,
                             y = bill_depth_mm,
                             group = species)) +
  geom_point(aes(color = species,
                 shape = species),
             size = 3,
             alpha = 0.8) +
  geom_smooth(method = "lm", se = FALSE, aes(color = species)) +

bmass_hist <- ggplot(data = penguins, aes(x = body_mass_g)) +
  geom_histogram(aes(fill = species),
                 alpha = 0.5,
                 position = "identity") +
  scale_fill_manual(values = c("darkorange","purple","cyan4")) +
  theme_minimal() +
  labs(x = "Body mass (g)",
       y = "Frequency",
       title = "Penguin body mass")

mass_hist

library(ggdist)      ## add uncertainity visualizations to ggplot2

theme_set(theme_classic()) 

## calculate bill ratio and summary stats
  penguins %>% 
  mutate(bill_ratio = bill_length_mm / bill_depth_mm) %>% 
  filter(!is.na(bill_ratio)) %>% 
  group_by(species) %>% 
  mutate(
    n = n(),
    median = median(bill_ratio),
    max = max(bill_ratio)
  ) %>% 
  ungroup() %>% 
  mutate(species_num = as.numeric(fct_rev(species))) %>% 
  ggplot(aes(bill_ratio, species_num, color = species)) +
  stat_summary(
    geom = "linerange",
    fun.min = function(x) -Inf,
    fun.max = function(x) median(x, na.rm = TRUE),
    linetype = "dotted",
    orientation = "y",
    size = .7
  ) +
  geom_point(
    aes(y = species_num - .15), 
    shape = "|",
    size = 5,
    alpha = .33
  ) +
  ggdist::stat_halfeye(
    aes(
      y = species_num,
      color = species,
      fill = after_scale(colorspace::lighten(color, .5))
    ),
    shape = 18,
    point_size = 3,
    interval_size = 1.8,
    adjust = .5,
    .width = c(0, 1)
  ) +
  geom_text(
    aes(x = median, label = format(round(median, 2), nsmall = 2)),
    stat = "unique",
    color = "white",
    family = "Open Sans",
    fontface = "bold",
    size = 3.4,
    nudge_y = .15
  ) +
  geom_text(
    aes(x = max, label = glue::glue("n = {n}")),
    stat = "unique",
    family = "Open Sans",
    fontface = "bold",
    size = 3.5,
    hjust = 0,
    nudge_x = .01,
    nudge_y = .02
  ) +
  coord_cartesian(clip = "off", expand = FALSE) +
  scale_x_continuous(
    limits = c(1.6, 3.8),
    breaks = seq(1.6, 3.8, by = .2)
  ) +
  scale_y_continuous(
    limits = c(.55, NA),
    breaks = 1:3,
    labels = c("Gentoo", "Chinstrap", "Adélie")
  ) +
  scale_color_manual(values = c("#3d6721", "#a86826", "#006c89"), guide = "none") +
  scale_fill_manual(values = c("#3d6721", "#a86826", "#006c89"), guide = "none") +
  labs(
    x = "Bill ratio",
    y = NULL,
    subtitle = "B. Raincloud plot showing the distribution of bill ratios, estimated as bill length divided by bill depth.",
    caption = "Data: Gorman, Williams & Fraser (2014) *PLoS ONE* • Illustration: Allison Horst"
  ) +
  theme(
    panel.grid.major.x = element_line(size = .35),
    panel.grid.major.y = element_blank(),
    axis.text.y = element_text(size = 13),
    axis.ticks.length = unit(0, "lines"),
    plot.title.position = 'plot',
    plot.subtitle = element_text(margin = margin(t = 5, b = 10)),
    plot.margin = margin(10, 25, 10, 25)
  )